Weather forecast services in urban areas face an increasingly hard task of alerting the population to extreme weather events. The hardness of the problem is due to the dynamics of the phenomenon, which challenges numerical weather prediction models.

Machine Learning (ML) based models that may learn complex mappings between input-output from data. Weather forecasting has progressed from a very human-intensive effort to now being highly enabled by computation. The first big advance was numerical weather prediction (NWP), i.e., integrating the equations of motion forward in time with good initial conditions. But the more recent improvements have come from applying AI techniques to improve forecasting and to enable large quantities of machine-based forecasts. 

One of the early successes of using AI in weather forecasting was the Dynamical Integrated Forecast (DICast) System. DICast builds on several concepts that mimic the human forecasting decision process. It leverages the NWP model output and historical observations at the site for the forecast. It begins by correcting the output of each NWP model according to past performance.  

DICast has been applied to predict the major variables of interest (such as temperature, dew point, wind speed, irradiance, and probability of precipitation) at sites worldwide.  

Extreme weather prediction 

What if you could tell where the next big flood would hit or which region could be ravaged by a drought? AI can help you with this. 

Advances in supercomputing and AI mean this will soon become a reality. Each visualizations Engines, or EVE, compiles all the data we amass to allow governments and private citizens to mitigate against the effects of climate change. 

The EVE summit in Berlin is bringing together 180 of the world’s finest climate scientists from 29 different nations to try and do just that. EVE is an effort to engage the most powerful technologies to solve the biggest problems. More specifically, it is a way of bringing large-scale computing and AI to aid people in understanding what climate change means.  

Relying on an algorithm to predict extreme weather events and the impact of climate change seems like turning science fiction into science fact. According to Bjorn Stevens, a climate scientist who is the driving force behind the summit, if enormous amounts of information about possible climate futures and a tool to summarize it are created, like predicting the 15 biggest floods in the next 100 years according to these models in an area, it would help visualize. 

Reducing risk 

Wang Yi, the Vice Chair of the National Expert Panel on Climate Change of China, stated that cooperation is a significant factor in facing this global challenge, so we must take common actions. Joining forces will aid in innovating better and reducing risks. 

Compiling data and identifying places at risk is vital amid the growing list of threats we face. According to the UN, if the world’s temperature rises by 2 degrees Celsius by the end of this century, we will experience a fivefold global increase in floods, storms, drought and heat waves. Identifying the areas at risk can help in reducing the effects of global warming.

Conclusion 

AI will be able to have a meaningful impact on predicting climate change and extreme weather conditions. As humans become more confident in AI, we can rely on technology more to understand climate change and make more accurate predictions and models. This will allow humanity to be more targeted in our strategies to mitigate the worst effects. 

CGTN News